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--- |
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language: hi |
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datasets: |
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- common_voice |
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- indic tts |
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- iiith |
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metrics: |
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- wer |
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tags: |
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- audio |
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- automatic-speech-recognition |
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- speech |
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- xlsr-fine-tuning-week |
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license: apache-2.0 |
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model-index: |
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- name: Hindi XLSR Wav2Vec2 Large 53 |
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results: |
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- task: |
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name: Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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- name: Common Voice hi |
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type: common_voice |
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args: hi |
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- name: Indic IIT (IITM) |
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type: indic |
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args: hi |
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- name: IIITH Indic Dataset |
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type: iiith |
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args: hi |
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metrics: |
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- name: Custom Dataset Hindi WER |
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type: wer |
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value: 17.23 |
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- name: CommonVoice Hindi (Test) WER |
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type: wer |
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value: 56.46 |
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--- |
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# Wav2Vec2-Large-XLSR-53-Hindi |
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Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Hindi using the following datasets: |
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- [Common Voice](https://huggingface.co/datasets/common_voice), |
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- [Indic TTS- IITM](https://www.iitm.ac.in/donlab/tts/index.php) and |
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- [IIITH - Indic Speech Datasets](http://speech.iiit.ac.in/index.php/research-svl/69.html) |
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The Indic datasets are well balanced across gender and accents. However the CommonVoice dataset is skewed towards male voices |
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Fine-tuned on facebook/wav2vec2-large-xlsr-53 using Hindi dataset :: 60 epochs >> 17.05% WER |
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When using this model, make sure that your speech input is sampled at 16kHz. |
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## Usage |
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The model can be used directly (without a language model) as follows: |
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```python |
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import torch |
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import torchaudio |
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from datasets import load_dataset |
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from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor |
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test_dataset = load_dataset("common_voice", "hi", split="test") |
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processor = Wav2Vec2Processor.from_pretrained("skylord/wav2vec2-large-xlsr-hindi") |
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model = Wav2Vec2ForCTC.from_pretrained("skylord/wav2vec2-large-xlsr-hindi") |
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resampler = torchaudio.transforms.Resample(48_000, 16_000) |
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# Preprocessing the datasets. |
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# We need to read the aduio files as arrays |
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def speech_file_to_array_fn(batch): |
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speech_array, sampling_rate = torchaudio.load(batch["path"]) |
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batch["speech"] = resampler(speech_array).squeeze().numpy() |
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return batch |
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test_dataset = test_dataset.map(speech_file_to_array_fn) |
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inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True) |
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with torch.no_grad(): |
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logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits |
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predicted_ids = torch.argmax(logits, dim=-1) |
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print("Prediction:", processor.batch_decode(predicted_ids)) |
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print("Reference:", test_dataset["sentence"][:2]) |
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``` |
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## Predictions |
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*Some good ones ..... * |
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| Predictions | Reference | |
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|-------|-------| |
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|फिर वो सूरज तारे पहाड बारिश पदछड़ दिन रात शाम नदी बर्फ़ समुद्र धुंध हवा कुछ भी हो सकती है | फिर वो सूरज तारे पहाड़ बारिश पतझड़ दिन रात शाम नदी बर्फ़ समुद्र धुंध हवा कुछ भी हो सकती है | |
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| इस कारण जंगल में बडी दूर स्थित राघव के आश्रम में लोघ कम आने लगे और अधिकांश भक्त सुंदर के आश्रम में जाने लगे | इस कारण जंगल में बड़ी दूर स्थित राघव के आश्रम में लोग कम आने लगे और अधिकांश भक्त सुन्दर के आश्रम में जाने लगे | |
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| अपने बचन के अनुसार शुभमूर्त पर अनंत दक्षिणी पर्वत गया और मंत्रों का जप करके सरोवर में उतरा | अपने बचन के अनुसार शुभमुहूर्त पर अनंत दक्षिणी पर्वत गया और मंत्रों का जप करके सरोवर में उतरा | |
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*Some crappy stuff .... * |
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| Predictions | Reference | |
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|-------|-------| |
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| वस गनिल साफ़ है। | उसका दिल साफ़ है। | |
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| चाय वा एक कुछ लैंगे हब | चायवाय कुछ लेंगे आप | |
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| टॉम आधे है स्कूल हें है | टॉम अभी भी स्कूल में है | |
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## Evaluation |
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The model can be evaluated as follows on the following two datasets: |
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1. Custom dataset created from 20% of Indic, IIITH and CV (test): WER 17.xx% |
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2. CommonVoice Hindi test dataset: WER 56.xx% |
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Links to the datasets are provided above (check the links at the start of the README) |
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train-test csv files are shared on the following gdrive links: |
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a. IIITH [train](https://storage.googleapis.com/indic-dataset/train_test_splits/iiit_hi_train.csv) [test](https://storage.googleapis.com/indic-dataset/train_test_splits/iiit_hi_test.csv) |
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b. Indic TTS [train](https://storage.googleapis.com/indic-dataset/train_test_splits/indic_train_full.csv) [test](https://storage.googleapis.com/indic-dataset/train_test_splits/indic_test_full.csv) |
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Update the audio_path as per your local file structure. |
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```python |
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import torch |
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import torchaudio |
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from datasets import load_dataset, load_metric |
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from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor |
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import re |
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## Load the datasets |
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test_dataset = load_dataset("common_voice", "hi", split="test") |
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indic = load_dataset("csv", data_files= {'train':"/workspace/data/hi2/indic_train_full.csv", |
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"test": "/workspace/data/hi2/indic_test_full.csv"}, download_mode="force_redownload") |
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iiith = load_dataset("csv", data_files= {"train": "/workspace/data/hi2/iiit_hi_train.csv", |
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"test": "/workspace/data/hi2/iiit_hi_test.csv"}, download_mode="force_redownload") |
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## Pre-process datasets and concatenate to create test dataset |
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# Drop columns of common_voice |
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split = ['train', 'test', 'validation', 'other', 'invalidated'] |
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for sp in split: |
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common_voice[sp] = common_voice[sp].remove_columns(['client_id', 'up_votes', 'down_votes', 'age', 'gender', 'accent', 'locale', 'segment']) |
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common_voice = common_voice.rename_column('path', 'audio_path') |
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common_voice = common_voice.rename_column('sentence', 'target_text') |
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train_dataset = datasets.concatenate_datasets([indic['train'], iiith['train'], common_voice['train']]) |
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test_dataset = datasets.concatenate_datasets([indic['test'], iiith['test'], common_voice['test'], common_voice['validation']]) |
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## Load model from HF hub |
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wer = load_metric("wer") |
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processor = Wav2Vec2Processor.from_pretrained("skylord/wav2vec2-large-xlsr-hindi") |
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model = Wav2Vec2ForCTC.from_pretrained("skylord/wav2vec2-large-xlsr-hindi") |
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model.to("cuda") |
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chars_to_ignore_regex = '[\,\?\.\!\-\'\;\:\"\“\%\‘\”\�Utrnle\_]' |
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unicode_ignore_regex = r'[dceMaWpmFui\xa0\u200d]' # Some unwanted unicode chars |
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resampler = torchaudio.transforms.Resample(48_000, 16_000) |
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# Preprocessing the datasets. |
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# We need to read the aduio files as arrays |
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def speech_file_to_array_fn(batch): |
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batch["target_text"] = re.sub(chars_to_ignore_regex, '', batch["target_text"]) |
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batch["target_text"] = re.sub(unicode_ignore_regex, '', batch["target_text"]) |
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speech_array, sampling_rate = torchaudio.load(batch["audio_path"]) |
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batch["speech"] = resampler(speech_array).squeeze().numpy() |
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return batch |
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test_dataset = test_dataset.map(speech_file_to_array_fn) |
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# Preprocessing the datasets. |
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# We need to read the aduio files as arrays |
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def evaluate(batch): |
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inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) |
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with torch.no_grad(): |
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logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits |
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pred_ids = torch.argmax(logits, dim=-1) |
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batch["pred_strings"] = processor.batch_decode(pred_ids) |
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return batch |
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result = test_dataset.map(evaluate, batched=True, batch_size=8) |
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print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"]))) |
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``` |
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**Test Result on custom dataset**: 17.23 % |
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```python |
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import torch |
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import torchaudio |
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from datasets import load_dataset, load_metric |
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from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor |
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import re |
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test_dataset = load_dataset("common_voice", "hi", split="test") |
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wer = load_metric("wer") |
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processor = Wav2Vec2Processor.from_pretrained("skylord/wav2vec2-large-xlsr-hindi") |
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model = Wav2Vec2ForCTC.from_pretrained("skylord/wav2vec2-large-xlsr-hindi") |
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model.to("cuda") |
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chars_to_ignore_regex = '[\,\?\.\!\-\'\;\:\"\“\%\‘\”\�Utrnle\_]' |
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unicode_ignore_regex = r'[dceMaWpmFui\xa0\u200d]' # Some unwanted unicode chars |
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resampler = torchaudio.transforms.Resample(48_000, 16_000) |
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# Preprocessing the datasets. |
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# We need to read the aduio files as arrays |
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def speech_file_to_array_fn(batch): |
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batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).sub(unicode_ignore_regex, '', batch["sentence"]) |
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speech_array, sampling_rate = torchaudio.load(batch["path"]) |
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batch["speech"] = resampler(speech_array).squeeze().numpy() |
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return batch |
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test_dataset = test_dataset.map(speech_file_to_array_fn) |
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# Preprocessing the datasets. |
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# We need to read the aduio files as arrays |
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def evaluate(batch): |
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inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) |
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with torch.no_grad(): |
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logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits |
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pred_ids = torch.argmax(logits, dim=-1) |
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batch["pred_strings"] = processor.batch_decode(pred_ids) |
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return batch |
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result = test_dataset.map(evaluate, batched=True, batch_size=8) |
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print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"]))) |
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``` |
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**Test Result on CommonVoice**: 56.46 % |
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## Training |
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The Common Voice `train`, `validation`, datasets were used for training as well as |
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The script used for training & wandb dashboard can be found [here](https://wandb.ai/thinkevolve/huggingface/reports/Project-Hindi-XLSR-Large--Vmlldzo2MTI2MTQ) |
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